Classification of Chocolate Consumption Using Support Vector Machine Algorithm

  • Firman Aziz Universitas Pancasakti
  • Jeffry Jeffry Institut Teknologi Bacharuddin Jusuf Habibie
  • Nur Ayu Asrhi Universitas Pancasakti Makassar, Indonesia
  • Supriyadi La Wungo STMIK Kreatindo Manokwari
Keywords: Classification, Chocolate, Support Vector Machine Algorithm, SMOTE, Imbalanced Data

Abstract

Chocolate, derived from the processing of cocoa beans (Theobroma cacao), is a widely consumed product with potential health risks when consumed excessively. This study investigates the classification of chocolate consumption behaviors using the Support Vector Machine (SVM) algorithm and evaluates its classification performance. A benchmark dataset on chocolate consumption was employed, partitioned into nine folds for training and testing purposes. To mitigate issues related to data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The experimental findings indicate that SVM, enhanced by SMOTE, demonstrates a reliable capacity for classifying chocolate consumption categories. Performance evaluation across multiple experiments revealed variations in Accuracy, Precision, Recall, and F1-Score, with overall accuracies ranging from 50% to 60%, suggesting moderate but consistent classification performance.

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Published
2025-04-30
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